SoroushVahidi/scidocs huggingface dataset:
Ethical theory, codes of ethics and IS practice
(1996)

Ethical issues, with respect to computer-based information systems, are important to the individual IS practitioner. These same issues also have an important impact on the moral well-being of organizations and societies. Considerable discussion has taken place in the Information Systems (IS) literature on specific ethical issues, but there is little published work which relates these issues to mainstream ethical theory. This paper describes a range of ethical theories drawn from philosophy literature and uses these theories to critique aspects of the newly revised ACM Code of Ethics. Some in the IS field on problematic ethical issues which are not resolved by the Code are then identified and discussed. The paper draws some implications and conclusions on the value of ethical theory and codes of practice, and on further work to develop existing ethical themes and to promote new initiatives in the ethical domain.
Dual-camera design for coded aperture snapshot spectral imaging.
(2015)

Coded aperture snapshot spectral imaging (CASSI) provides an efficient mechanism for recovering 3D spectral data from a single 2D measurement. However, since the reconstruction problem is severely underdetermined, the quality of recovered spectral data is usually limited. In this paper we propose a novel dual-camera design to improve the performance of CASSI while maintaining its snapshot advantage. Specifically, a beam splitter is placed in front of the objective lens of CASSI, which allows the same scene to be simultaneously captured by a grayscale camera. This uncoded grayscale measurement, in conjunction with the coded CASSI measurement, greatly eases the reconstruction problem and yields high-quality 3D spectral data. Both simulation and experimental results demonstrate the effectiveness of the proposed method.
Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets
(2008)

C product reviews have proliferated online, driven by the notion that consumers’ decision to purchase or not purchase a product is based on the positive or negative information about that product they obtain from fellow consumers. Using research on information processing as a foundation, we suggest that in the context of an online community, reviewer disclosure of identity-descriptive information is used by consumers to supplement or replace product information when making purchase decisions and evaluating the helpfulness of online reviews. Using a unique data set based on both chronologically compiled ratings as well as reviewer characteristics for a given set of products and geographical location-based purchasing behavior from Amazon, we provide evidence that community norms are an antecedent to reviewer disclosure of identity-descriptive information. Online community members rate reviews containing identity-descriptive information more positively, and the prevalence of reviewer disclosure of identity information is associated with increases in subsequent online product sales. In addition, we show that shared geographical location increases the relationship between disclosure and product sales, thus highlighting the important role of geography in electronic commerce. Taken together, our results suggest that identity-relevant information about reviewers shapes community members’ judgment of products and reviews. Implications for research on the relationship between online word-of-mouth (WOM) and sales, peer recognition and reputation systems, and conformity to online community norms are discussed.
Examining CNN Representations With Respect to Dataset Bias
(2018)

Given a pre-trained CNN without any testing samples, this paper proposes a simple yet effective method to diagnose feature representations of the CNN. We aim to discover representation flaws caused by potential dataset bias. More specifically, when the CNN is trained to estimate image attributes, we mine latent relationships between representations of different attributes inside the CNN. Then, we compare the mined attribute relationships with ground-truth attribute relationships to discover the CNN’s blind spots and failure modes due to dataset bias. In fact, representation flaws caused by dataset bias cannot be examined by conventional evaluation strategies based on testing images, because testing images may also have a similar bias. Experiments have demonstrated the effectiveness of our method.
A hybrid Fuzzy Logic Controller-Firefly Algorithm (FLC-FA) based for MPPT Photovoltaic (PV) system in solar car
(2016)

This paper propose Firefly Algorithm (FA) new method for tuning the membership function of fuzzy logic controller for Maximum Power Point Tracker (MPPT) system Photovoltaic solar car, that consist of hybrid Fuzzy Logic Controller - Firefly Algorithm (FLC-FA) for the parameter. There are many MPPT methods for photovoltaic (PV) system, Perturbation and Observation (PnO), fuzzy logic controller (FLC) standard, and hybrid Fuzzy logic controller firefly algorithm (FLC-FA) is compared in this paper. The proposed FLC-FA algorithm is to obtain the optimal solution for MPPT for photovoltaic (PV) systems for solar cars. The result Fuzzy logic controller firefly (FLC-FA) of the proposed method, the highest maximum strength and efficiency generated is PnO = 96.31%, Standard FLC = 99.88% and Proposed FLC-FA = 99.98%. better than PnO and Fuzzy logic controller standard method. The main advantage of the proposed FLC-FA is more efficient and accurate than the still fuzzy logic controller standard.
A review and future direction of agile, business intelligence, analytics and data science
(2016)

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.
Ravenclaw: dialog management using hierarchical task decomposition and an expectation agenda
(2003)

We describe RavenClaw, a new dialog management framework developed as a successor to the Agenda [1] architecture used in the CMU Communicator. RavenClaw introduces a clear separation between task and discourse behavior specification, and allows rapid development of dialog management components for spoken dialog systems operating in complex, goal-oriented domains. The system development effort is focused entirely on the specification of the dialog task, while a rich set of domain-independent conversational behaviors are transparently generated by the dialog engine. To date, RavenClaw has been applied to five different domains allowing us to draw some preliminary conclusions as to the generality of the approach. We briefly describe our experience in developing these systems.
Automatically clustering similar units for unit selection in speech synthesis
(1997)

This paper describes a new method for synthesizing speech by concatenating sub-word units from a database of labelled speech. A large unit inventory is created by automatically clustering units of the same phone class based on their phonetic and prosodic context. The appropriate cluster is then selected for a target unit offering a small set of candidate units. An optimal path is found through the candidate units based on their distance from the cluster center and an acoustically based join cost. Details of the method and justification are presented. The results of experiments using two different databases are given, optimising various parameters within the system. Also a comparison with other existing selection based synthesis techniques is given showing the advantages this method has over existing ones. The method is implemented within a full text-to-speech system offering efficient natural sounding speech synthesis.
Creating natural dialogs in the carnegie mellon communicator system
(1999)

The Carnegie Mellon Communicator system helps users create complex travel itineraries through a conversational interface. Itineraries consist of (multi-leg) flights, hotel and car reservations and are built from actual travel information for North America, obtained from the Web. The system manages dialog using a schema-based approach. Schemas correspond to major units of task information (such as a flight leg) and define conversational topics, or foci of interaction, meaningful to the user.
Extraction of high confidence minutiae points from fingerprint images
(2015)

Fingerprint is one of the well accepted biometric traits for building automatic human recognition system. These systems mainly involves matching of two fingerprints based on their minutiae points. Therefore, extraction of reliable or true minutiae from the fingerprint image is critically important. This paper proposed an novel algorithm for automatic extraction of highly reliable minutiae points from a fingerprint image. It utilizes frequency domain image enhancement and heuristics. Experiments have been conducted on two databases. FVC2002 which is a publicly available fingerprint database containing 800 fingerprint images of 100 persons and IITK-Sel500FP which is an in-house database containing 1000 fingerprint images of 500 persons. Minutiae points extracted by the proposed algorithm are benchmarked against the manually marked ones and the comparison is done with an open source minutiae extractor mindtct of NIST. Experimental results have shown that the proposed algorithm has significantly higher confidence of extracted minutiae points being true.
Cognitive effort drives workspace configuration of human brain functional networks.
(2011)

Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16-32 Hz) and γ-band (32-63 Hz) networks, compared with both lower (α-band; 8-16 Hz) and higher (high γ-band; 63-125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.
Designing wireless transceiver blocks for LoRa application
(2017)

The Internet of Things enables connected objects to communicate over distant fields, and trade-offs are often observed with the battery life and range. Long Range Radio, or LoRa, provides transmission for up to 15 km with impressive battery life, while sacrificing throughput. In order to design the LoRa system, there is a need to design and implement the LoRa transceiver. 1 V, 900 MHz, LoRa-capable transceiver blocks were designed in 65 nm process. The receiver front end consists of the narrowband low noise amplifier, common gate-common source balun, and double balanced mixer. The power consumption of this system is 2.63 mW. The overall gain of the receiver front end is approximately 51 dB. The transmitter implemented in this project utilizes a class-D amplifier alongside an inverter chain driver stage and a transformer based power combiner. The transmitter outputs 21.57 dBm of power from the given supply at 900 MHz, operating at 21% drain efficiency.
Network Monitoring as a Streaming Analytics Problem
(2016)

Programmable switches potentially make it easier to perform flexible network monitoring queries at line rate, and scalable stream processors make it possible to fuse data streams to answer more sophisticated queries about the network in real-time. However, processing such network monitoring queries at high traffic rates requires both the switches and the stream processors to filter the traffic iteratively and adaptively so as to extract only that traffic that is of interest to the query at hand. While the realization that network monitoring is a streaming analytics problem has been made earlier, our main contribution in this paper is the design and implementation of Sonata, a closed-loop system that enables network operators to perform streaming analytics for network monitoring applications at scale. To achieve this objective, Sonata allows operators to express a network monitoring query by considering each packet as a tuple. More importantly, Sonata allows them to partition the query across both the switches and the stream processor, and through iterative refinement, Sonata's runtime attempts to extract only the traffic that pertains to the query, thus ensuring that the stream processor can scale to satisfy a large number of queries for traffic at very high rates. We show with a simple example query involving DNS reflection attacks and traffic traces from one of the world's largest IXPs that Sonata can capture 95% of all traffic pertaining to the query, while reducing the overall data rate by a factor of about 400 and the number of required counters by four orders of magnitude.
Neural representations for object perception: structure, category, and adaptive coding.
(2011)

Object perception is one of the most remarkable capacities of the primate brain. Owing to the large and indeterminate dimensionality of object space, the neural basis of object perception has been difficult to study and remains controversial. Recent work has provided a more precise picture of how 2D and 3D object structure is encoded in intermediate and higher-level visual cortices. Yet, other studies suggest that higher-level visual cortex represents categorical identity rather than structure. Furthermore, object responses are surprisingly adaptive to changes in environmental statistics, implying that learning through evolution, development, and also shorter-term experience during adulthood may optimize the object code. Future progress in reconciling these findings will depend on more effective sampling of the object domain and direct comparison of these competing hypotheses.
A Wireless Headstage for Combined Optogenetics and Multichannel Electrophysiological Recording
(2017)

This paper presents a wireless headstage with real-time spike detection and data compression for combined optogenetics and multichannel electrophysiological recording. The proposed headstage, which is intended to perform both optical stimulation and electrophysiological recordings simultaneously in freely moving transgenic rodents, is entirely built with commercial off-the-shelf components, and includes 32 recording channels and 32 optical stimulation channels. It can detect, compress and transmit full action potential waveforms over 32 channels in parallel and in real time using an embedded digital signal processor based on a low-power field programmable gate array and a Microblaze microprocessor softcore. Such a processor implements a complete digital spike detector featuring a novel adaptive threshold based on a Sigma-delta control loop, and a wavelet data compression module using a new dynamic coefficient re-quantization technique achieving large compression ratios with higher signal quality. Simultaneous optical stimulation and recording have been performed in-vivo using an optrode featuring 8 microelectrodes and 1 implantable fiber coupled to a 465-nm LED, in the somatosensory cortex and the Hippocampus of a transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line 4) under anesthetized conditions. Experimental results show that the proposed headstage can trigger neuron activity while collecting, detecting and compressing single cell microvolt amplitude activity from multiple channels in parallel while achieving overall compression ratios above 500. This is the first reported high-channel count wireless optogenetic device providing simultaneous optical stimulation and recording. Measured characteristics show that the proposed headstage can achieve up to 100% of true positive detection rate for signal-to-noise ratio (SNR) down to 15 dB, while achieving up to 97.28% at SNR as low as 5 dB. The implemented prototype features a lifespan of up to 105 minutes, and uses a lightweight (2.8 g) and compact  $(17 \times 18 \times 10\ \text{mm}^{3})$ rigid-flex printed circuit board.
Big Data and Transformational Government
(2013)

The big data phenomenon is growing throughout private and public sector domains. Profit motives make it urgent for companies in the private sector to learn how to leverage big data. However, in the public sector, government services could also be greatly improved through the use of big data. Here, the authors describe some drivers, barriers, and best practices affecting the use of big data and associated analytics in the government domain. They present a model that illustrates how big data can result in transformational government through increased efficiency and effectiveness in the delivery of services. Their empirical basis for this model uses a case vignette from the US Department of Veterans Affairs, while the theoretical basis is a balanced view of big data that takes into account the continuous growth and use of such data. This article is part of a special issue on big data and business analytics.
An analysis and critique of Research through Design: towards a formalization of a research approach
(2010)

The field of HCI is experiencing a growing interest in Research through Design (RtD), a research approach that employs methods and processes from design practice as a legitimate method of inquiry. We are interested in expanding and formalizing this research approach, and understanding how knowledge, or theory, is generated from this type of design research. We conducted interviews with 12 leading HCI design researchers, asking them about design research, design theory, and RtD specifically. They were easily able to identify different types of design research and design theory from contemporary and historical design research efforts, and believed that RtD might be one of the most important contributions of design researchers to the larger research community. We further examined three historical RtD projects that were repeatedly mentioned in the interviews, and performed a critique of current RtD practices within the HCI research and interaction design communities. While our critique summarizes the problems, it also shows possible directions for further developments and refinements of the approach.
SDN On-The-Go (OTG) physical testbed
(2017)

An emerging field of research, Software Defined Networks (SDN) promises to change the landscape of traditional network topology and management. Researchers and early adopters alike need adequate SDN testing facilities for their experiments but their options are limited. Industry is responding slowly with embedded support for SDN in their enterprise grade network hardware but it is cost prohibitive for many test environments with a single SDN switch costing thousands of dollars. There are a few emerging community SDN test networks that are fantastic for testing large topologies with production grade traffic but there is a cost associated with membership and some controlled experiments are difficult. A free and indispensible alternative to a dedicated hardware SDN is to use network emulation tools. These software tools are widely used and invaluable to SDN research. They provide an amazingly precise representation of physical network nodes and behavior but are inherently limited by their aggregation with other virtual devices on the same compute node. Some of our research requires a higher precision than software emulation can provide. Our solution is to build a low cost, portable, standalone SDN testbed. Called SDN On-The-Go (OTG), it is a complete, self-contained testbed that consists of four dedicated ZodiacFX SDN switches, four RaspberryPi3 hosts, a dedicated Kangaroo+ controller with 4GB RAM and a couple of routers to form the network isolation. The testbed supports many configurations for pseudo real-world SDN experiments that produce reliable and repeatable results. It can be used as a standalone research tool or as part of a larger network with production quality traffic. SDN OTG is designed to be used as a portable teaching device, moved from classroom to classroom or taken home for private research. We achieved our repeatability factor of an order of magnitude greater than emulation based testing. Our SDN OTG physical testbed weighs only twenty pounds, costs about a thousand US dollars, provides repeatable, precise time sensitive data and can be setup as a fully functional SDN testbed in a matter of minutes.
Predicting and Improving Memory Retention : Psychological Theory Matters in the Big Data Era
(2016)

Cognitive psychology has long had the aim of understanding mechanisms of human memory, with the expectation that such an understanding will yield practical techniques that support learning and retention. Although research insights have given rise to qualitative advice for students and educators, we present a complementary approach that offers quantitative, individualized guidance. Our approach synthesizes theory-driven and data-driven methodologies. Psychological theory characterizes basic mechanisms of human memory shared among members of a population, whereas machine-learning techniques use observations from a population to make inferences about individuals. We argue that despite the power of big data, psychological theory provides essential constraints on models. We present models of forgetting and spaced practice that predict the dynamic time-varying knowledge state of an individual student for specific material. We incorporate these models into retrieval-practice software to assist students in reviewing previously mastered material. In an ambitious year-long intervention in a middle-school foreign language course, we demonstrate the value of systematic review on long-term educational outcomes, but more specifically, the value of adaptive review that leverages data from a population of learners to personalize recommendations based on an individual’s study history and past performance.